PUMA
Istituto di Scienza e Tecnologie dell'Informazione     
Pennacchioli D., Rossetti G., Pappalardo L., Pedreschi D., Giannotti F., Coscia M. The three dimensions of social prominence. In: SocInfo2013 - Social Informatics. 5th International Conference (Kyoto, Japan, 25-27 November 2013). Proceedings, pp. 319 - 332. Adam Jatowt, Ee-Peng Lim, Ying Ding, Asako Miura, Taro Tezuka, GaŽl Dias, Katsumi Tanaka, Andrew Flanagin, Bing Tian Dai (eds.). (Lecture Notes in Computer Science, vol. 8238). Springer, 2013.
 
 
Abstract
(English)
One classic problem de nition in social network analysis is the study of di usion in networks, which enables us to tackle problems like favoring the adoption of positive technologies. Most of the attention has been turned to how to maximize the number of in uenced nodes, but this approach misses the fact that di erent scenarios imply di erent dif- fusion dynamics, only slightly related to maximizing the number of nodes involved. In this paper we measure three di erent dimensions of social prominence: the Width, i.e. the ratio of neighbors in uenced by a node; the Depth, i.e. the degrees of separation from a node to the nodes perceiv- ing its prominence; and the Strength, i.e. the intensity of the prominence of a node. By de ning a procedure to extract prominent users in complex networks, we detect associations between the three dimensions of social prominence and classical network statistics. We validate our results on a social network extracted from the Last.Fm music platform.
URL: http://link.springer.com/chapter/10.1007/978-3-319-03260-3_28
DOI: 10.1007/978-3-319-03260-3_28
Subject Complex networks
Data mining
Community discovery
H.2.8 Database applications


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